A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Improved LDA method for better classification and dimensionality reduction.
New methods correct spectral distortions using known analyte concentrations.
Improved spectral clustering for community detection in networks.
New spectral clustering method for graphs with uneven node degrees.
The paper corrects for node degree in spectral clustering using random walk Laplacian.
Introduces a new spectral geometry framework with dissipative data.
Deep neural networks correct Mie scattering in FTIR spectra of biological samples.
Spectral clustering is a fast and popular algorithm for finding clusters in networks. Recently, Chaudhuri et al. (2012) and Amini et al.(2012) proposed inspired variations on the algorithm that artificially inflate the node degrees for improved statistical performance. The current paper extends the previous statistical…
Study on spectral asymptotics in elasticity on smooth manifolds.
A fast spectral algorithm detects community structure in evolving graphs.
The dictionary-aided sparse regression (SR) approach has recently emerged as a promising alternative to hyperspectral unmixing (HU) in remote sensing. By using an available spectral library as a dictionary, the SR approach identifies the underlying materials in a given hyperspectral image by selecting a small subset of…
We consider community detection in Degree-Corrected Stochastic Block Models (DC-SBM). We propose a spectral clustering algorithm based on a suitably normalized adjacency matrix. We show that this algorithm consistently recovers the block-membership of all but a vanishing fraction of nodes, in the regime where the lowes…
Corrects errors in previous work on spectral asymptotics in elasticity.
Paper characterizes optimal graph clustering limits under a new model.
Proposes a new algorithm to estimate invariant subspaces across multilayer networks.
Randomized spectral co-clustering speeds up large-scale directed networks.
We describe a relation between Atiyah-Patodi-Singer boundary condition and a global elliptic boundary condition which naturally appears in formulating a splitting formula for a spectral flow, when we decompose the manifold into two components. Then we give a variant of the splitting formula with the Hoermander index as…
A new method for community detection in networks is presented.
We analyze the performance of spectral clustering for community extraction in stochastic block models. We show that, under mild conditions, spectral clustering applied to the adjacency matrix of the network can consistently recover hidden communities even when the order of the maximum expected degree is as small as $\l…
Corrects errors in previous work on linear elasticity calculations.
Vertex clustering in a stochastic blockmodel graph has wide applicability and has been the subject of extensive research. In thispaper, we provide a short proof that the adjacency spectral embedding can be used to obtain perfect clustering for the stochastic blockmodel and the degree-corrected stochastic blockmodel. We…
SC-Net learns interpretable filters for inverse problems, achieving optimal convergence and super-resolution.
Corrected graph convolutions improve node classification on graphs.
SEDA improves RLDA for high-dimensional data.
Spectral Adaptive Conformal Prediction for Structured Non-Exchangeable Data
Dual regularized graph Laplacian improves spectral clustering for community detection.
New algorithm recovers communities in broader network models.
This paper presents a margin-based multiclass generalization bound for neural networks that scales with their margin-normalized "spectral complexity": their Lipschitz constant, meaning the product of the spectral norms of the weight matrices, times a certain correction factor. This bound is empirically investigated for…
Algorithm learns dynamics from past observations.
We establish a splitting formula for the spectral flow of the odd signature operator on a closed 3-manifold M coupled to a path of SU(2) connections, provided M = S cup X, where S is the solid torus. It describes the spectral flow on M in terms of the spectral flow on S, the spectral flow on X (with certain Atiyah-Pato…
Researchers calculate exact moduli for type II flux backgrounds using spectral sequences.
Spectral clustering has been one of the widely used methods for community detection in networks. However, large-scale networks bring computational challenges to the eigenvalue decomposition therein. In this paper, we study the spectral clustering using randomized sketching algorithms from a statistical perspective, whe…
Supervised topic models simultaneously model the latent topic structure of large collections of documents and a response variable associated with each document. Existing inference methods are based on variational approximation or Monte Carlo sampling, which often suffers from the local minimum defect. Spectral methods …
New framework detects directional influence in multivariate time series.
We describe a machine-learning approach to pitch correcting a solo singing performance in a karaoke setting, where the solo voice and accompaniment are on separate tracks. The proposed approach addresses the situation where no musical score of the vocals nor the accompaniment exists: It predicts the amount of correctio…
Community detection is a fundamental problem in network analysis which is made more challenging by overlaps between communities which often occur in practice. Here we propose a general, flexible, and interpretable generative model for overlapping communities, which can be thought of as a generalization of the degree-co…
The paper proves spectral convergence rates for graph Laplacian to manifold Laplace-Beltrami operator.
Sparse spectral decomposition identifies overlapping communities in networks.
The problem of image restoration in cryo-EM entails correcting for the effects of the Contrast Transfer Function (CTF) and noise. Popular methods for image restoration include `phase flipping', which corrects only for the Fourier phases but not amplitudes, and Wiener filtering, which requires the spectral signal to noi…
In the context of clustering, we consider a generative model in a Euclidean ambient space with clusters of different shapes, dimensions, sizes and densities. In an asymptotic setting where the number of points becomes large, we obtain theoretical guaranties for a few emblematic methods based on pairwise distances: a si…
Quantizes symplectic manifolds with bounded geometry using Berezin-Toeplitz method.
Method selects number of communities in weighted networks.
A statistical functional, such as the mean or the median, is called elicitable if there is a scoring function or loss function such that the correct forecast of the functional is the unique minimizer of the expected score. Such scoring functions are called strictly consistent for the functional. The elicitability of a …
An intriguing phenomenon observed during training neural networks is the spectral bias, which states that neural networks are biased towards learning less complex functions. The priority of learning functions with low complexity might be at the core of explaining generalization ability of neural network, and certain ef…
Spectral clustering is one of the most popular, yet still incompletely understood, methods for community detection on graphs. This article studies spectral clustering based on the Bethe-Hessian matrix for sparse heterogeneous graphs (following the degree-corrected stochastic block model) in a …
New method for mixed memberships using symmetrized Laplacian inverse matrix.
The paper analyzes the variance of different shuffling methods in stochastic gradient descent.
It was pointed out to us that the proof of a crucial lemma (Lemma 5.3) in the paper is incorrect. Thus the approximation theorem (Theorem 0.1) for L^2 torsion of an amenable covering of a finite simplicial complex remains unproved. However, results and proofs of the first four sections (in particular, the approximation…